Machine-learning technology has shown the ability to obtain valuable information from multi-band photometric data. Several studies have made some efforts to determine stellar parameters from photometric surveys. [1]} has derived stellar effective temperatures from \(Gaia\)  data release two, using a random forest (RF) algorithm. [2]} has presented random forest models to perform STAR-GALAXY-QSO classification and obtained the effective temperature of stars. [3]} developed a scheme to achieve stellar parameters from the LASSO algorithm and Support Vector Regression (SVR) models. [4]} designed a cost-sensitive artificial neural network and achieved stellar parameters from two million stars of J-PLUS first data release (DR1). [5]} developed a Stellar Parameters Estimation based on the Ensemble Methods pipeline (SPEEM), which is a stack of feature searching, normalization, and multi-output regressor. The SPEEM is based on RF and extreme gradient boosting (XGB). These works are all based on machine-learning algorithms, indicating their powerful ability to reveal potential patterns within data.
